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Marine Microplastics Detection Using Compressive Raman Spectroscopy

In a recent paper published in Environmental Technology & Innovation, researchers from France introduced an innovative technology called the compressive Raman micro-spectroscopy technique (CRT). This technology can detect and classify various types of marine microplastic (MP) particles across a broad area much faster than previous methods.

Marine Microplastics Detection Using Compressive Raman Spectroscopy

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Microplastics (MPs), defined as plastic particles smaller than 5 mm, pose a significant threat to marine ecosystems and human health. They can be ingested by marine organisms, causing internal damage, digestive obstructions, and potentially fatal consequences.

Therefore, accurate detection and identification of these particles are crucial for understanding the scope of plastic pollution and devising effective mitigation strategies.

Raman spectroscopy, a technique leveraging light-matter interaction, offers molecular insights into the composition, structure, and orientation of materials. It can differentiate between various types of plastics, including polyethylene, polypropylene, polystyrene, polyurethane, and polyvinyl chloride.

However, conventional Raman spectroscopy faces challenges such as low signal-to-noise ratio, lengthy integration times, and extensive data processing requirements. These limitations hinder the imaging and classification of MPs in environmental samples, especially when mixed with other organic or inorganic substances.

About the Research

In this study, the authors aimed to develop, validate, and demonstrate the efficacy of CRT technology for the rapid detection and classification of six different types of MPs commonly found in the natural marine environment.

These particles, collected from various locations in Brittany, France, included polyethylene, polystyrene, polyethylene terephthalate, polyurethane, polypropylene, and nylon. The samples were cryo-ground to produce microparticles of various sizes and colors, effectively replicating real-world environmental conditions.

The CRT setup included a 785 nm continuous-wave laser, a custom-built spectrometer with a digital micromirror device for binary filter display, and a single-photon avalanche photodiode serving as the detector. The binary filters were meticulously designed to enhance the precision of estimating chemical species proportions, using known Raman spectra of the target MP particles.

Operating on the principle of compressive sensing, CRT leverages the sparseness of Raman spectra to minimize the number of measurements needed for spectrum reconstruction. Additionally, this technology employs binary spectral filters and single-pixel detection to achieve minimal pixel dwell time Raman signal detection, enabling rapid imaging and classification of MPs.

Research Findings

Initially, the authors demonstrated the imaging and classification capabilities of the CRT system using a simplified model of two types of synthetic MP beads: polystyrene and methyl methacrylate.

They successfully classified these species with a remarkably low pixel dwell time of 250 μs, a significant improvement over conventional Raman imaging, which typically requires 10-100 times longer.

The CRT was then used to image a mixed sample containing different types of MPs collected from marine environments. It effectively classified various types of MPs, including those with different colors and fluorescence backgrounds, within a 300 μm × 300 μm field of view.

Fine-tuning the pixel dwell times showed that a velocity of 250 μs per pixel/filter (resulting in a total pixel dwell time of 1.75 ms) maintained a sufficient signal-to-noise ratio, ensuring accurate classification. The methodology achieved an impressive 98.6 % accuracy in classifying MP particles and a notable 97.8 % precision in quantifying them, surpassing state-of-the-art techniques.

To further demonstrate the potential of CRT, imaging was conducted over a larger area, including a 1 mm² silicone filter substrate on which MPs were deposited. Remarkably, this wide area was imaged in just 2 hours and 10 minutes, achieving a spatial resolution of 1 μm. This represented a significant improvement over the 10-100 hours typically required for conventional Raman imaging of similar-sized areas.


The rapid detection and classification capability of CRT for MPs in marine environments holds significant implications for environmental monitoring and research. By swiftly acquiring data over large areas, it facilitates effective mapping and quantification of MPs across diverse aquatic ecosystems.

This capability is crucial for understanding the impact of plastic pollution. Its versatility in handling samples with varying fluorescence backgrounds and colors makes it a promising tool for investigating MPs in complex, real-world environmental settings.


The novel CRT approach demonstrated its efficacy in detecting and distinguishing MPs. It successfully handled complex and heterogeneous samples, such as natural marine specimens, without requiring sample preparation or pre-processing. The researchers emphasized that as plastic pollution continues to grow, their methodology could be used to develop solutions and monitor the success of mitigation efforts.

Future work should focus on optimizing CRT processes, exploring new applications in environmental monitoring, and developing dynamic monitoring systems for real-time analysis of MPs in flowing water samples. These advancements could further enhance the understanding and management of plastic pollution in marine ecosystems.

Journal Reference

Grand, C., et al. (2024). Fast compressive Raman micro-spectroscopy to image and classify microplastics from natural marine environment. Environmental Technology & Innovation.

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Muhammad Osama

Written by

Muhammad Osama

Muhammad Osama is a full-time data analytics consultant and freelance technical writer based in Delhi, India. He specializes in transforming complex technical concepts into accessible content. He has a Bachelor of Technology in Mechanical Engineering with specialization in AI & Robotics from Galgotias University, India, and he has extensive experience in technical content writing, data science and analytics, and artificial intelligence.


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